126 citations · 403 across the 35 of their papers we have counts for
5 papers · 1 filter
Generalized Resubstitution for Regression Error Estimation
Diego Marcondes, Ulisses Braga-Neto
We propose generalized resubstitution error estimators for regression, a broad family of estimators, each corresponding to a choice of empirical probability measures and loss funct…
DeepOSets: Non-Autoregressive In-Context Learning with Permutation-Invariance Inductive Bias
Shao-Ting Chiu, Junyuan Hong, Ulisses Braga-Neto
In-context learning (ICL) is the remarkable ability displayed by some machine learning models to learn from examples provided in a user prompt without any model parameter updates.…
Label Propagation for Physics-Informed Neural Networks and Physics-Informed Gaussian Processes
Ming Zhong, Dehao Liu, Raymundo Arroyave +1
We present a series of empirical results of the application of semi-supervised label propagation techniques in training physics-informed machine learning methods. This includes sel…
Stability in Training PINNs for Stiff PDEs: Why Initial Conditions Matter
Baoli Hao, Chun Liu, Ulisses Braga-Neto +2
Training physics-informed neural networks (PINNs) on stiff, time-dependent PDEs remains a fundamental challenge due to optimization instabilities and gradient pathologies. Through…
Physics-Informed Neural Networks for Multi-Phase Flow in Porous Media Considering Dual Shocks and Interphase Solubility
Jingjing Zhang, Ulisses Braga-Neto, Eduardo Gildin
Physics-Informed Neural Networks (PINNs) integrate physical principles into machine learning, finding wide applications in various science and engineering fields. However, solving…